With the rapid development of the civil aviation industry, the in-depth analysis of civil aircraft O&M (operation and maintenance) data has become a critical pathway to enhance flight safety and maintenance efficiency. To address the dual demands of entity recognition from unstructured O&M texts and user intent understanding, a multi-task collaborative analysis framework was proposed. First, semantic information was captured through pre-training with the RoBERTa (robustly optimized BERT approach) model. Subsequently, BiLSTM (bidirectional long short-term memory) networks were employed to extract temporal dependency features. The CRF (conditional random field) was then applied to optimize label transition constraints and generate the final label sequence, achieving high-precision recognition of entities such as components, faults, and operations in maintenance records. Second, to meet the semantic understanding requirements of intelligent Q&A (question and answering) systems, a comparative analysis was conducted on the adaptability of the BERT, RoBERTa, and Qwen-Max pre-trained language models in the task of operation and maintenance intent recognition. Through performance-efficiency trade-off validation, RoBERTa is selected as the backbone model for intent classification. Experimental results demonstrate that the proposed framework significantly outperforms traditional baseline models in both entity extraction and intent recognition tasks. Its dual-task collaboration mechanism provides structured data support for constructing civil aircraft O&M knowledge graphs and intelligent Q&A services, exhibiting substantial application potential and practical value.